Melancholic depression prediction by identifying representative features in metabolic and microarray profiles with missing values.

PubMed

Nie, Zhi; Yang, Tao; Liu, Yashu; Li, Qingyang; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

2015-01-01

Recent studies have revealed that melancholic depression, one major subtype of depression, is closely associated with the concentration of some metabolites and biological functions of certain genes and pathways. Meanwhile, recent advances in biotechnologies have allowed us to collect a large amount of genomic data, e.g., metabolites and microarray gene expression. With such a huge amount of information available, one approach that can give us new insights into the understanding of the fundamental biology underlying melancholic depression is to build disease status prediction models using classification or regression methods. However, the existence of strong empirical correlations, e.g., those exhibited by genes sharing the same biological pathway in microarray profiles, tremendously limits the performance of these methods. Furthermore, the occurrence of missing values which are ubiquitous in biomedical applications further complicates the problem. In this paper, we hypothesize that the problem of missing values might in some way benefit from the correlation between the variables and propose a method to learn a compressed set of representative features through an adapted version of sparse coding which is capable of identifying correlated variables and addressing the issue of missing values simultaneously. An efficient algorithm is also developed to solve the proposed formulation. We apply the proposed method on metabolic and microarray profiles collected from a group of subjects consisting of both patients with melancholic depression and healthy controls. Results show that the proposed method can not only produce meaningful clusters of variables but also generate a set of representative features that achieve superior classification performance over those generated by traditional clustering and data imputation techniques. In particular, on both datasets, we found that in comparison with the competing algorithms, the representative features learned by the proposed method give rise to significantly improved sensitivity scores, suggesting that the learned features allow prediction with high accuracy of disease status in those who are diagnosed with melancholic depression. To our best knowledge, this is the first work that applies sparse coding to deal with high feature correlations and missing values, which are common challenges in many biomedical applications. The proposed method can be readily adapted to other biomedical applications involving incomplete and high-dimensional data.

Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure.

PubMed

Zhang, Wen; Xiao, Fan; Li, Bin; Zhang, Siguang

2016-01-01

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.

Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure

PubMed Central

Xiao, Fan; Li, Bin; Zhang, Siguang

2016-01-01

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods. PMID:27579031

Cross-Domain Multi-View Object Retrieval via Multi-Scale Topic Models.

PubMed

Hong, Richang; Hu, Zhenzhen; Wang, Ruxin; Wang, Meng; Tao, Dacheng

2016-09-27

The increasing number of 3D objects in various applications has increased the requirement for effective and efficient 3D object retrieval methods, which attracted extensive research efforts in recent years. Existing works mainly focus on how to extract features and conduct object matching. With the increasing applications, 3D objects come from different areas. In such circumstances, how to conduct object retrieval becomes more important. To address this issue, we propose a multi-view object retrieval method using multi-scale topic models in this paper. In our method, multiple views are first extracted from each object, and then the dense visual features are extracted to represent each view. To represent the 3D object, multi-scale topic models are employed to extract the hidden relationship among these features with respected to varied topic numbers in the topic model. In this way, each object can be represented by a set of bag of topics. To compare the objects, we first conduct topic clustering for the basic topics from two datasets, and then generate the common topic dictionary for new representation. Then, the two objects can be aligned to the same common feature space for comparison. To evaluate the performance of the proposed method, experiments are conducted on two datasets. The 3D object retrieval experimental results and comparison with existing methods demonstrate the effectiveness of the proposed method.

Improving performances of suboptimal greedy iterative biclustering heuristics via localization.

PubMed

Erten, Cesim; Sözdinler, Melih

2010-10-15

Biclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. Even the simplest versions of the problem are computationally hard. Most of the proposed solutions therefore employ greedy iterative heuristics that locally optimize a suitably assigned scoring function. We provide a fast and simple pre-processing algorithm called localization that reorders the rows and columns of the input data matrix in such a way as to group correlated entries in small local neighborhoods within the matrix. The proposed localization algorithm takes its roots from effective use of graph-theoretical methods applied to problems exhibiting a similar structure to that of biclustering. In order to evaluate the effectivenesss of the localization pre-processing algorithm, we focus on three representative greedy iterative heuristic methods. We show how the localization pre-processing can be incorporated into each representative algorithm to improve biclustering performance. Furthermore, we propose a simple biclustering algorithm, Random Extraction After Localization (REAL) that randomly extracts submatrices from the localization pre-processed data matrix, eliminates those with low similarity scores, and provides the rest as correlated structures representing biclusters. We compare the proposed localization pre-processing with another pre-processing alternative, non-negative matrix factorization. We show that our fast and simple localization procedure provides similar or even better results than the computationally heavy matrix factorization pre-processing with regards to H-value tests. We next demonstrate that the performances of the three representative greedy iterative heuristic methods improve with localization pre-processing when biological correlations in the form of functional enrichment and PPI verification constitute the main performance criteria. The fact that the random extraction method based on localization REAL performs better than the representative greedy heuristic methods under same criteria also confirms the effectiveness of the suggested pre-processing method. Supplementary material including code implementations in LEDA C++ library, experimental data, and the results are available at http://code.google.com/p/biclustering/ cesim@khas.edu.tr; melihsozdinler@boun.edu.tr Supplementary data are available at Bioinformatics online.

Analysing the Image Building Effects of TV Advertisements Using Internet Community Data

NASA Astrophysics Data System (ADS)

Uehara, Hiroshi; Sato, Tadahiko; Yoshida, Kenichi

This paper proposes a method to measure the effects of TV advertisements on the Internet bulletin boards. It aims to clarify how the viewes' interests on TV advertisements are reflected on their images on the promoted products. Two kinds of time series data are generated based on the proposed method. First one represents the time series fluctuation of the interests on the TV advertisements. Another one represents the time series fluctuation of the images on the products. By analysing the correlations between these two time series data, we try to clarify the implicit relationship between the viewer's interests on the TV advertisement and their images on the promoted products. By applying the proposed method to an Internet bulletin board that deals with certain cosmetic brand, we show that the images on the products vary depending on the difference of the interests on each TV advertisement.

Induced subgraph searching for geometric model fitting

NASA Astrophysics Data System (ADS)

Xiao, Fan; Xiao, Guobao; Yan, Yan; Wang, Xing; Wang, Hanzi

2017-11-01

In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the "qualified" subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.

Skeleton-based human action recognition using multiple sequence alignment

NASA Astrophysics Data System (ADS)

Ding, Wenwen; Liu, Kai; Cheng, Fei; Zhang, Jin; Li, YunSong

2015-05-01

Human action recognition and analysis is an active research topic in computer vision for many years. This paper presents a method to represent human actions based on trajectories consisting of 3D joint positions. This method first decompose action into a sequence of meaningful atomic actions (actionlets), and then label actionlets with English alphabets according to the Davies-Bouldin index value. Therefore, an action can be represented using a sequence of actionlet symbols, which will preserve the temporal order of occurrence of each of the actionlets. Finally, we employ sequence comparison to classify multiple actions through using string matching algorithms (Needleman-Wunsch). The effectiveness of the proposed method is evaluated on datasets captured by commodity depth cameras. Experiments of the proposed method on three challenging 3D action datasets show promising results.

Advanced soft computing diagnosis method for tumour grading.

PubMed

Papageorgiou, E I; Spyridonos, P P; Stylios, C D; Ravazoula, P; Groumpos, P P; Nikiforidis, G N

2006-01-01

To develop an advanced diagnostic method for urinary bladder tumour grading. A novel soft computing modelling methodology based on the augmentation of fuzzy cognitive maps (FCMs) with the unsupervised active Hebbian learning (AHL) algorithm is applied. One hundred and twenty-eight cases of urinary bladder cancer were retrieved from the archives of the Department of Histopathology, University Hospital of Patras, Greece. All tumours had been characterized according to the classical World Health Organization (WHO) grading system. To design the FCM model for tumour grading, three experts histopathologists defined the main histopathological features (concepts) and their impact on grade characterization. The resulted FCM model consisted of nine concepts. Eight concepts represented the main histopathological features for tumour grading. The ninth concept represented the tumour grade. To increase the classification ability of the FCM model, the AHL algorithm was applied to adjust the weights of the FCM. The proposed FCM grading model achieved a classification accuracy of 72.5%, 74.42% and 95.55% for tumours of grades I, II and III, respectively. An advanced computerized method to support tumour grade diagnosis decision was proposed and developed. The novelty of the method is based on employing the soft computing method of FCMs to represent specialized knowledge on histopathology and on augmenting FCMs ability using an unsupervised learning algorithm, the AHL. The proposed method performs with reasonably high accuracy compared to other existing methods and at the same time meets the physicians' requirements for transparency and explicability.

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation

PubMed Central

Alacid, Beatriz

2018-01-01

This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Terma sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images. PMID:29316716

Tensor-based Dictionary Learning for Spectral CT Reconstruction

PubMed Central

Zhang, Yanbo; Wang, Ge

2016-01-01

Spectral computed tomography (CT) produces an energy-discriminative attenuation map of an object, extending a conventional image volume with a spectral dimension. In spectral CT, an image can be sparsely represented in each of multiple energy channels, and are highly correlated among energy channels. According to this characteristics, we propose a tensor-based dictionary learning method for spectral CT reconstruction. In our method, tensor patches are extracted from an image tensor, which is reconstructed using the filtered backprojection (FBP), to form a training dataset. With the Candecomp/Parafac decomposition, a tensor-based dictionary is trained, in which each atom is a rank-one tensor. Then, the trained dictionary is used to sparsely represent image tensor patches during an iterative reconstruction process, and the alternating minimization scheme is adapted for optimization. The effectiveness of our proposed method is validated with both numerically simulated and real preclinical mouse datasets. The results demonstrate that the proposed tensor-based method generally produces superior image quality, and leads to more accurate material decomposition than the currently popular popular methods. PMID:27541628

SUSTAINABLE SANITATION FOR THE HÔPITAL SACRÉ COEUR IN MILOT, HAITI

EPA Science Inventory

The proposed design represents a completely sustainable system and a significant improvement on the current pit-based disposal method that causes groundwater contamination and health and environmental hazards during floods. The proposed latrine will capture possible contaminan...

SEIPS-based process modeling in primary care.

PubMed

Wooldridge, Abigail R; Carayon, Pascale; Hundt, Ann Schoofs; Hoonakker, Peter L T

2017-04-01

Process mapping, often used as part of the human factors and systems engineering approach to improve care delivery and outcomes, should be expanded to represent the complex, interconnected sociotechnical aspects of health care. Here, we propose a new sociotechnical process modeling method to describe and evaluate processes, using the SEIPS model as the conceptual framework. The method produces a process map and supplementary table, which identify work system barriers and facilitators. In this paper, we present a case study applying this method to three primary care processes. We used purposeful sampling to select staff (care managers, providers, nurses, administrators and patient access representatives) from two clinics to observe and interview. We show the proposed method can be used to understand and analyze healthcare processes systematically and identify specific areas of improvement. Future work is needed to assess usability and usefulness of the SEIPS-based process modeling method and further refine it. Copyright © 2016 Elsevier Ltd. All rights reserved.

SEIPS-Based Process Modeling in Primary Care

PubMed Central

Wooldridge, Abigail R.; Carayon, Pascale; Hundt, Ann Schoofs; Hoonakker, Peter

2016-01-01

Process mapping, often used as part of the human factors and systems engineering approach to improve care delivery and outcomes, should be expanded to represent the complex, interconnected sociotechnical aspects of health care. Here, we propose a new sociotechnical process modeling method to describe and evaluate processes, using the SEIPS model as the conceptual framework. The method produces a process map and supplementary table, which identify work system barriers and facilitators. In this paper, we present a case study applying this method to three primary care processes. We used purposeful sampling to select staff (care managers, providers, nurses, administrators and patient access representatives) from two clinics to observe and interview. We show the proposed method can be used to understand and analyze healthcare processes systematically and identify specific areas of improvement. Future work is needed to assess usability and usefulness of the SEIPS-based process modeling method and further refine it. PMID:28166883

Representativeness of environmental impact assessment methods regarding Life Cycle Inventories.

PubMed

Esnouf, Antoine; Latrille, Éric; Steyer, Jean-Philippe; Helias, Arnaud

2018-04-15

Life Cycle Assessment (LCA) characterises all the exchanges between human driven activities and the environment, thus representing a powerful approach for tackling the environmental impact of a production system. However, LCA practitioners must still choose the appropriate Life Cycle Impact Assessment (LCIA) method to use and are expected to justify this choice: impacts should be relevant facing the concerns of the study and misrepresentations should be avoided. This work aids practitioners in evaluating the adequacy between the assessed environmental issues and studied production system. Based on a geometrical standpoint of LCA framework, Life Cycle Inventories (LCIs) and LCIA methods were localized in the vector space spanned by elementary flows. A proximity measurement, the Representativeness Index (RI), is proposed to explore the relationship between those datasets (LCIs and LCIA methods) through an angular distance. RIs highlight LCIA methods that measure issues for which the LCI can be particularly harmful. A high RI indicates a close proximity between a LCI and a LCIA method, and highlights a better representation of the elementary flows by the LCIA method. To illustrate the benefits of the proposed approach, representativeness of LCIA methods regarding four electricity mix production LCIs from the ecoinvent database are presented. RIs for 18 LCIA methods (accounting for a total of 232 impact categories) were calculated on these LCIs and the relevance of the methods are discussed. RIs prove to be a criterion for distinguishing the different LCIA methods and could thus be employed by practitioners for deeper interpretations of LCIA results. Copyright © 2017 Elsevier B.V. All rights reserved.

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

PubMed

Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

2005-04-15

The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

Salient object detection: manifold-based similarity adaptation approach

NASA Astrophysics Data System (ADS)

Zhou, Jingbo; Ren, Yongfeng; Yan, Yunyang; Gao, Shangbing

2014-11-01

A saliency detection algorithm based on manifold-based similarity adaptation is proposed. The proposed algorithm is divided into three steps. First, we segment an input image into superpixels, which are represented as the nodes in a graph. Second, a new similarity measurement is used in the proposed algorithm. The weight matrix of the graph, which indicates the similarities between the nodes, uses a similarity-based method. It also captures the manifold structure of the image patches, in which the graph edges are determined in a data adaptive manner in terms of both similarity and manifold structure. Then, we use local reconstruction method as a diffusion method to obtain the saliency maps. The objective function in the proposed method is based on local reconstruction, with which estimated weights capture the manifold structure. Experiments on four bench-mark databases demonstrate the accuracy and robustness of the proposed method.

Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang

2016-09-01

For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.

New equivalent-electrical circuit model and a practical measurement method for human body impedance.

PubMed

Chinen, Koyu; Kinjo, Ichiko; Zamami, Aki; Irei, Kotoyo; Nagayama, Kanako

2015-01-01

Human body impedance analysis is an effective tool to extract electrical information from tissues in the human body. This paper presents a new measurement method of impedance using armpit electrode and a new equivalent circuit model for the human body. The lowest impedance was measured by using an LCR meter and six electrodes including armpit electrodes. The electrical equivalent circuit model for the cell consists of resistance R and capacitance C. The R represents electrical resistance of the liquid of the inside and outside of the cell, and the C represents high frequency conductance of the cell membrane. We propose an equivalent circuit model which consists of five parallel high frequency-passing CR circuits. The proposed equivalent circuit represents alpha distribution in the impedance measured at a lower frequency range due to ion current of the outside of the cell, and beta distribution at a high frequency range due to the cell membrane and the liquid inside cell. The calculated values by using the proposed equivalent circuit model were consistent with the measured values for the human body impedance.

10 CFR 63.332 - Representative volume.

Code of Federal Regulations, 2011 CFR

2011-01-01

... accessible environment; (2) Its position and dimensions in the aquifer are determined using average... determined by site characterization; and (3) It contains 3,000 acre-feet of water (about 3,714,450,000 liters... dimensions of the representative volume. The DOE must propose its chosen method, and any underlying...

10 CFR 63.332 - Representative volume.

Code of Federal Regulations, 2010 CFR

2010-01-01

... accessible environment; (2) Its position and dimensions in the aquifer are determined using average... determined by site characterization; and (3) It contains 3,000 acre-feet of water (about 3,714,450,000 liters... dimensions of the representative volume. The DOE must propose its chosen method, and any underlying...

Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets.

PubMed

Li, Der-Chiang; Hu, Susan C; Lin, Liang-Sian; Yeh, Chun-Wu

2017-01-01

It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers are overly influenced by the larger classes and ignore the smaller ones. As a result, the classification algorithms often have poor learning performances due to slow convergence in the smaller classes. To balance such data sets, this paper presents a strategy that involves reducing the sizes of the majority data and generating synthetic samples for the minority data. In the reducing operation, we use the box-and-whisker plot approach to exclude outliers and the Mega-Trend-Diffusion method to find representative data from the majority data. To generate the synthetic samples, we propose a counterintuitive hypothesis to find the distributed shape of the minority data, and then produce samples according to this distribution. Four real datasets were used to examine the performance of the proposed approach. We used paired t-tests to compare the Accuracy, G-mean, and F-measure scores of the proposed data pre-processing (PPDP) method merging in the D3C method (PPDP+D3C) with those of the one-sided selection (OSS), the well-known SMOTEBoost (SB) study, and the normal distribution-based oversampling (NDO) approach, and the proposed data pre-processing (PPDP) method. The results indicate that the classification performance of the proposed approach is better than that of above-mentioned methods.

Strengths and weaknesses of temporal stability analysis for monitoring and estimating grid-mean soil moisture in a high-intensity irrigated agricultural landscape

NASA Astrophysics Data System (ADS)

Ran, Youhua; Li, Xin; Jin, Rui; Kang, Jian; Cosh, Michael H.

2017-01-01

Monitoring and estimating grid-mean soil moisture is very important for assessing many hydrological, biological, and biogeochemical processes and for validating remotely sensed surface soil moisture products. Temporal stability analysis (TSA) is a valuable tool for identifying a small number of representative sampling points to estimate the grid-mean soil moisture content. This analysis was evaluated and improved using high-quality surface soil moisture data that were acquired by a wireless sensor network in a high-intensity irrigated agricultural landscape in an arid region of northwestern China. The performance of the TSA was limited in areas where the representative error was dominated by random events, such as irrigation events. This shortcoming can be effectively mitigated by using a stratified TSA (STSA) method, proposed in this paper. In addition, the following methods were proposed for rapidly and efficiently identifying representative sampling points when using TSA. (1) Instantaneous measurements can be used to identify representative sampling points to some extent; however, the error resulting from this method is significant when validating remotely sensed soil moisture products. Thus, additional representative sampling points should be considered to reduce this error. (2) The calibration period can be determined from the time span of the full range of the grid-mean soil moisture content during the monitoring period. (3) The representative error is sensitive to the number of calibration sampling points, especially when only a few representative sampling points are used. Multiple sampling points are recommended to reduce data loss and improve the likelihood of representativeness at two scales.

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

PubMed Central

Lu, Hongyang; Wei, Jingbo; Wang, Yuhao; Deng, Xiaohua

2016-01-01

Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values. PMID:27110235

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction.

PubMed

Lu, Hongyang; Wei, Jingbo; Liu, Qiegen; Wang, Yuhao; Deng, Xiaohua

2016-01-01

Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

Analytical investigation of different mathematical approaches utilizing manipulation of ratio spectra

NASA Astrophysics Data System (ADS)

Osman, Essam Eldin A.

2018-01-01

This work represents a comparative study of different approaches of manipulating ratio spectra, applied on a binary mixture of ciprofloxacin HCl and dexamethasone sodium phosphate co-formulated as ear drops. The proposed new spectrophotometric methods are: ratio difference spectrophotometric method (RDSM), amplitude center method (ACM), first derivative of the ratio spectra (1DD) and mean centering of ratio spectra (MCR). The proposed methods were checked using laboratory-prepared mixtures and were successfully applied for the analysis of pharmaceutical formulation containing the cited drugs. The proposed methods were validated according to the ICH guidelines. A comparative study was conducted between those methods regarding simplicity, limitations and sensitivity. The obtained results were statistically compared with those obtained from the reported HPLC method, showing no significant difference with respect to accuracy and precision.

A Novel Clustering Method Curbing the Number of States in Reinforcement Learning

NASA Astrophysics Data System (ADS)

Kotani, Naoki; Nunobiki, Masayuki; Taniguchi, Kenji

We propose an efficient state-space construction method for a reinforcement learning. Our method controls the number of categories with improving the clustering method of Fuzzy ART which is an autonomous state-space construction method. The proposed method represents weight vector as the mean value of input vectors in order to curb the number of new categories and eliminates categories whose state values are low to curb the total number of categories. As the state value is updated, the size of category becomes small to learn policy strictly. We verified the effectiveness of the proposed method with simulations of a reaching problem for a two-link robot arm. We confirmed that the number of categories was reduced and the agent achieved the complex task quickly.

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling.

PubMed

Wu, Hulin; Lu, Tao; Xue, Hongqi; Liang, Hua

2014-04-02

The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a sparse additive ODE (SA-ODE) model, coupled with ODE estimation methods and adaptive group LASSO techniques, to model dynamic GRNs that could flexibly deal with nonlinear regulation effects. The asymptotic properties of the proposed method are established and simulation studies are performed to validate the proposed approach. An application example for identifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the usefulness of the proposed method.

An interval-valued 2-tuple linguistic group decision-making model based on the Choquet integral operator

NASA Astrophysics Data System (ADS)

Liu, Bingsheng; Fu, Meiqing; Zhang, Shuibo; Xue, Bin; Zhou, Qi; Zhang, Shiruo

2018-01-01

The Choquet integral (IL) operator is an effective approach for handling interdependence among decision attributes in complex decision-making problems. However, the fuzzy measures of attributes and attribute sets required by IL are difficult to achieve directly, which limits the application of IL. This paper proposes a new method for determining fuzzy measures of attributes by extending Marichal's concept of entropy for fuzzy measure. To well represent the assessment information, interval-valued 2-tuple linguistic context is utilised to represent information. Then, we propose a Choquet integral operator in an interval-valued 2-tuple linguistic environment, which can effectively handle the correlation between attributes. In addition, we apply these methods to solve multi-attribute group decision-making problems. The feasibility and validity of the proposed operator is demonstrated by comparisons with other models in illustrative example part.

A novel class sensitive hashing technique for large-scale content-based remote sensing image retrieval

NASA Astrophysics Data System (ADS)

Reato, Thomas; Demir, Begüm; Bruzzone, Lorenzo

2017-10-01

This paper presents a novel class sensitive hashing technique in the framework of large-scale content-based remote sensing (RS) image retrieval. The proposed technique aims at representing each image with multi-hash codes, each of which corresponds to a primitive (i.e., land cover class) present in the image. To this end, the proposed method consists of a three-steps algorithm. The first step is devoted to characterize each image by primitive class descriptors. These descriptors are obtained through a supervised approach, which initially extracts the image regions and their descriptors that are then associated with primitives present in the images. This step requires a set of annotated training regions to define primitive classes. A correspondence between the regions of an image and the primitive classes is built based on the probability of each primitive class to be present at each region. All the regions belonging to the specific primitive class with a probability higher than a given threshold are highly representative of that class. Thus, the average value of the descriptors of these regions is used to characterize that primitive. In the second step, the descriptors of primitive classes are transformed into multi-hash codes to represent each image. This is achieved by adapting the kernel-based supervised locality sensitive hashing method to multi-code hashing problems. The first two steps of the proposed technique, unlike the standard hashing methods, allow one to represent each image by a set of primitive class sensitive descriptors and their hash codes. Then, in the last step, the images in the archive that are very similar to a query image are retrieved based on a multi-hash-code-matching scheme. Experimental results obtained on an archive of aerial images confirm the effectiveness of the proposed technique in terms of retrieval accuracy when compared to the standard hashing methods.

Efficient view based 3-D object retrieval using Hidden Markov Model

NASA Astrophysics Data System (ADS)

Jain, Yogendra Kumar; Singh, Roshan Kumar

2013-12-01

Recent research effort has been dedicated to view based 3-D object retrieval, because of highly discriminative property of 3-D object and has multi view representation. The state-of-art method is highly depending on their own camera array setting for capturing views of 3-D object and use complex Zernike descriptor, HAC for representative view selection which limit their practical application and make it inefficient for retrieval. Therefore, an efficient and effective algorithm is required for 3-D Object Retrieval. In order to move toward a general framework for efficient 3-D object retrieval which is independent of camera array setting and avoidance of representative view selection, we propose an Efficient View Based 3-D Object Retrieval (EVBOR) method using Hidden Markov Model (HMM). In this framework, each object is represented by independent set of view, which means views are captured from any direction without any camera array restriction. In this, views are clustered (including query view) to generate the view cluster, which is then used to build the query model with HMM. In our proposed method, HMM is used in twofold: in the training (i.e. HMM estimate) and in the retrieval (i.e. HMM decode). The query model is trained by using these view clusters. The EVBOR query model is worked on the basis of query model combining with HMM. The proposed approach remove statically camera array setting for view capturing and can be apply for any 3-D object database to retrieve 3-D object efficiently and effectively. Experimental results demonstrate that the proposed scheme has shown better performance than existing methods. [Figure not available: see fulltext.

Infrared and visible image fusion with spectral graph wavelet transform.

PubMed

Yan, Xiang; Qin, Hanlin; Li, Jia; Zhou, Huixin; Zong, Jing-guo

2015-09-01

Infrared and visible image fusion technique is a popular topic in image analysis because it can integrate complementary information and obtain reliable and accurate description of scenes. Multiscale transform theory as a signal representation method is widely used in image fusion. In this paper, a novel infrared and visible image fusion method is proposed based on spectral graph wavelet transform (SGWT) and bilateral filter. The main novelty of this study is that SGWT is used for image fusion. On the one hand, source images are decomposed by SGWT in its transform domain. The proposed approach not only effectively preserves the details of different source images, but also excellently represents the irregular areas of the source images. On the other hand, a novel weighted average method based on bilateral filter is proposed to fuse low- and high-frequency subbands by taking advantage of spatial consistency of natural images. Experimental results demonstrate that the proposed method outperforms seven recently proposed image fusion methods in terms of both visual effect and objective evaluation metrics.

Improved remote gaze estimation using corneal reflection-adaptive geometric transforms

NASA Astrophysics Data System (ADS)

Ma, Chunfei; Baek, Seung-Jin; Choi, Kang-A.; Ko, Sung-Jea

2014-05-01

Recently, the remote gaze estimation (RGE) technique has been widely applied to consumer devices as a more natural interface. In general, the conventional RGE method estimates a user's point of gaze using a geometric transform, which represents the relationship between several infrared (IR) light sources and their corresponding corneal reflections (CRs) in the eye image. Among various methods, the homography normalization (HN) method achieves state-of-the-art performance. However, the geometric transform of the HN method requiring four CRs is infeasible for the case when fewer than four CRs are available. To solve this problem, this paper proposes a new RGE method based on three alternative geometric transforms, which are adaptive to the number of CRs. Unlike the HN method, the proposed method not only can operate with two or three CRs, but can also provide superior accuracy. To further enhance the performance, an effective error correction method is also proposed. By combining the introduced transforms with the error-correction method, the proposed method not only provides high accuracy and robustness for gaze estimation, but also allows for a more flexible system setup with a different number of IR light sources. Experimental results demonstrate the effectiveness of the proposed method.

Proposed method to calculate FRMAC intervention levels for the assessment of radiologically contaminated food and comparison of the proposed method to the U.S. FDA's method to calculate derived intervention levels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kraus, Terrence D.; Hunt, Brian D.

This report reviews the method recommended by the U.S. Food and Drug Administration for calculating Derived Intervention Levels (DILs) and identifies potential improvements to the DIL calculation method to support more accurate ingestion pathway analyses and protective action decisions. Further, this report proposes an alternate method for use by the Federal Emergency Radiological Assessment Center (FRMAC) to calculate FRMAC Intervention Levels (FILs). The default approach of the FRMAC during an emergency response is to use the FDA recommended methods. However, FRMAC recommends implementing the FIL method because we believe it to be more technically accurate. FRMAC will only implement themore » FIL method when approved by the FDA representative on the Federal Advisory Team for Environment, Food, and Health.« less

Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy.

PubMed

Katić, Darko; Schuck, Jürgen; Wekerle, Anna-Laura; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie

2016-06-01

Computer assistance is increasingly common in surgery. However, the amount of information is bound to overload processing abilities of surgeons. We propose methods to recognize the current phase of a surgery for context-aware information filtering. The purpose is to select the most suitable subset of information for surgical situations which require special assistance. We combine formal knowledge, represented by an ontology, and experience-based knowledge, represented by training samples, to recognize phases. For this purpose, we have developed two different methods. Firstly, we use formal knowledge about possible phase transitions to create a composition of random forests. Secondly, we propose a method based on cultural optimization to infer formal rules from experience to recognize phases. The proposed methods are compared with a purely formal knowledge-based approach using rules and a purely experience-based one using regular random forests. The comparative evaluation on laparoscopic pancreas resections and adrenalectomies employs a consistent set of quality criteria on clean and noisy input. The rule-based approaches proved best with noisefree data. The random forest-based ones were more robust in the presence of noise. Formal and experience-based knowledge can be successfully combined for robust phase recognition.

Reduction in training time of a deep learning model in detection of lesions in CT

NASA Astrophysics Data System (ADS)

Makkinejad, Nazanin; Tajbakhsh, Nima; Zarshenas, Amin; Khokhar, Ashfaq; Suzuki, Kenji

2018-02-01

Deep learning (DL) emerged as a powerful tool for object detection and classification in medical images. Building a well-performing DL model, however, requires a huge number of images for training, and it takes days to train a DL model even on a cutting edge high-performance computing platform. This study is aimed at developing a method for selecting a "small" number of representative samples from a large collection of training samples to train a DL model for the could be used to detect polyps in CT colonography (CTC), without compromising the classification performance. Our proposed method for representative sample selection (RSS) consists of a K-means clustering algorithm. For the performance evaluation, we applied the proposed method to select samples for the training of a massive training artificial neural network based DL model, to be used for the classification of polyps and non-polyps in CTC. Our results show that the proposed method reduce the training time by a factor of 15, while maintaining the classification performance equivalent to the model trained using the full training set. We compare the performance using area under the receiveroperating- characteristic curve (AUC).

A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing

PubMed Central

Yu, Ning; Xiao, Chenxian; Wu, Yinfeng; Feng, Renjian

2016-01-01

Traditional radio-map-based localization methods need to sample a large number of location fingerprints offline, which requires huge amount of human and material resources. To solve the high sampling cost problem, an automatic radio-map construction algorithm based on crowdsourcing is proposed. The algorithm employs the crowd-sourced information provided by a large number of users when they are walking in the buildings as the source of location fingerprint data. Through the variation characteristics of users’ smartphone sensors, the indoor anchors (doors) are identified and their locations are regarded as reference positions of the whole radio-map. The AP-Cluster method is used to cluster the crowdsourced fingerprints to acquire the representative fingerprints. According to the reference positions and the similarity between fingerprints, the representative fingerprints are linked to their corresponding physical locations and the radio-map is generated. Experimental results demonstrate that the proposed algorithm reduces the cost of fingerprint sampling and radio-map construction and guarantees the localization accuracy. The proposed method does not require users’ explicit participation, which effectively solves the resource-consumption problem when a location fingerprint database is established. PMID:27070623

Efficiently modelling urban heat storage: an interface conduction scheme in an urban land surface model (aTEB v2.0)

NASA Astrophysics Data System (ADS)

Lipson, Mathew J.; Hart, Melissa A.; Thatcher, Marcus

2017-03-01

Intercomparison studies of models simulating the partitioning of energy over urban land surfaces have shown that the heat storage term is often poorly represented. In this study, two implicit discrete schemes representing heat conduction through urban materials are compared. We show that a well-established method of representing conduction systematically underestimates the magnitude of heat storage compared with exact solutions of one-dimensional heat transfer. We propose an alternative method of similar complexity that is better able to match exact solutions at typically employed resolutions. The proposed interface conduction scheme is implemented in an urban land surface model and its impact assessed over a 15-month observation period for a site in Melbourne, Australia, resulting in improved overall model performance for a variety of common material parameter choices and aerodynamic heat transfer parameterisations. The proposed scheme has the potential to benefit land surface models where computational constraints require a high level of discretisation in time and space, for example at neighbourhood/city scales, and where realistic material properties are preferred, for example in studies investigating impacts of urban planning changes.

A method to reproduce alpha-particle spectra measured with semiconductor detectors.

PubMed

Timón, A Fernández; Vargas, M Jurado; Sánchez, A Martín

2010-01-01

A method is proposed to reproduce alpha-particle spectra measured with silicon detectors, combining analytical and computer simulation techniques. The procedure includes the use of the Monte Carlo method to simulate the tracks of alpha-particles within the source and in the detector entrance window. The alpha-particle spectrum is finally obtained by the convolution of this simulated distribution and the theoretical distributions representing the contributions of the alpha-particle spectrometer to the spectrum. Experimental spectra from (233)U and (241)Am sources were compared with the predictions given by the proposed procedure, showing good agreement. The proposed method can be an important aid for the analysis and deconvolution of complex alpha-particle spectra. Copyright 2009 Elsevier Ltd. All rights reserved.

Macromolecule mapping of the brain using ultrashort-TE acquisition and reference-based metabolite removal.

PubMed

Lam, Fan; Li, Yudu; Clifford, Bryan; Liang, Zhi-Pei

2018-05-01

To develop a practical method for mapping macromolecule distribution in the brain using ultrashort-TE MRSI data. An FID-based chemical shift imaging acquisition without metabolite-nulling pulses was used to acquire ultrashort-TE MRSI data that capture the macromolecule signals with high signal-to-noise-ratio (SNR) efficiency. To remove the metabolite signals from the ultrashort-TE data, single voxel spectroscopy data were obtained to determine a set of high-quality metabolite reference spectra. These spectra were then incorporated into a generalized series (GS) model to represent general metabolite spatiospectral distributions. A time-segmented algorithm was developed to back-extrapolate the GS model-based metabolite distribution from truncated FIDs and remove it from the MRSI data. Numerical simulations and in vivo experiments have been performed to evaluate the proposed method. Simulation results demonstrate accurate metabolite signal extrapolation by the proposed method given a high-quality reference. For in vivo experiments, the proposed method is able to produce spatiospectral distributions of macromolecules in the brain with high SNR from data acquired in about 10 minutes. We further demonstrate that the high-dimensional macromolecule spatiospectral distribution resides in a low-dimensional subspace. This finding provides a new opportunity to use subspace models for quantification and accelerated macromolecule mapping. Robustness of the proposed method is also demonstrated using multiple data sets from the same and different subjects. The proposed method is able to obtain macromolecule distributions in the brain from ultrashort-TE acquisitions. It can also be used for acquiring training data to determine a low-dimensional subspace to represent the macromolecule signals for subspace-based MRSI. Magn Reson Med 79:2460-2469, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

New method for characterization of retroreflective materials

NASA Astrophysics Data System (ADS)

Junior, O. S.; Silva, E. S.; Barros, K. N.; Vitro, J. G.

2018-03-01

The present article aims to propose a new method of analyzing the properties of retroreflective materials using a goniophotometer. The aim is to establish a higher resolution test method with a wide range of viewing angles, taking into account a three-dimensional analysis of the retroreflection of the tested material. The validation was performed by collecting data from specimens collected from materials used in safety clothing and road signs. The approach showed that the results obtained by the proposed method are comparable to the results obtained by the normative protocols, representing an evolution for the metrology of these materials.

Glioma grading using cell nuclei morphologic features in digital pathology images

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Iftekharuddin, Khan M.

2016-03-01

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

PubMed

Aghamohammadi, Amirhossein; Ang, Mei Choo; A Sundararajan, Elankovan; Weng, Ng Kok; Mogharrebi, Marzieh; Banihashem, Seyed Yashar

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos

PubMed Central

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods. PMID:29438421

Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.

PubMed

Carrascal, A; Manrique, D; Ríos, J; Rossi, C

2003-01-01

This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach.

Adaptive target binarization method based on a dual-camera system

NASA Astrophysics Data System (ADS)

Lei, Jing; Zhang, Ping; Xu, Jiangtao; Gao, Zhiyuan; Gao, Jing

2018-01-01

An adaptive target binarization method based on a dual-camera system that contains two dynamic vision sensors was proposed. First, a preprocessing procedure of denoising is introduced to remove the noise events generated by the sensors. Then, the complete edge of the target is retrieved and represented by events based on an event mosaicking method. Third, the region of the target is confirmed by an event-to-event method. Finally, a postprocessing procedure of image open and close operations of morphology methods is adopted to remove the artifacts caused by event-to-event mismatching. The proposed binarization method has been extensively tested on numerous degraded images with nonuniform illumination, low contrast, noise, or light spots and successfully compared with other well-known binarization methods. The experimental results, which are based on visual and misclassification error criteria, show that the proposed method performs well and has better robustness on the binarization of degraded images.

Single image super-resolution based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia

2018-03-01

We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

A simple method for assessing occupational exposure via the one-way random effects model.

PubMed

Krishnamoorthy, K; Mathew, Thomas; Peng, Jie

2016-11-01

A one-way random effects model is postulated for the log-transformed shift-long personal exposure measurements, where the random effect in the model represents an effect due to the worker. Simple closed-form confidence intervals are proposed for the relevant parameters of interest using the method of variance estimates recovery (MOVER). The performance of the confidence bounds is evaluated and compared with those based on the generalized confidence interval approach. Comparison studies indicate that the proposed MOVER confidence bounds are better than the generalized confidence bounds for the overall mean exposure and an upper percentile of the exposure distribution. The proposed methods are illustrated using a few examples involving industrial hygiene data.

Novel Real-Time Facial Wound Recovery Synthesis Using Subsurface Scattering

PubMed Central

Chin, Seongah

2014-01-01

We propose a wound recovery synthesis model that illustrates the appearance of a wound healing on a 3-dimensional (3D) face. The H3 model is used to determine the size of the recovering wound. Furthermore, we present our subsurface scattering model that is designed to take the multilayered skin structure of the wound into consideration to represent its color transformation. We also propose a novel real-time rendering method based on the results of an analysis of the characteristics of translucent materials. Finally, we validate the proposed methods with 3D wound-simulation experiments using shading models. PMID:25197721

Jointly learning word embeddings using a corpus and a knowledge base

PubMed Central

Bollegala, Danushka; Maehara, Takanori; Kawarabayashi, Ken-ichi

2018-01-01

Methods for representing the meaning of words in vector spaces purely using the information distributed in text corpora have proved to be very valuable in various text mining and natural language processing (NLP) tasks. However, these methods still disregard the valuable semantic relational structure between words in co-occurring contexts. These beneficial semantic relational structures are contained in manually-created knowledge bases (KBs) such as ontologies and semantic lexicons, where the meanings of words are represented by defining the various relationships that exist among those words. We combine the knowledge in both a corpus and a KB to learn better word embeddings. Specifically, we propose a joint word representation learning method that uses the knowledge in the KBs, and simultaneously predicts the co-occurrences of two words in a corpus context. In particular, we use the corpus to define our objective function subject to the relational constrains derived from the KB. We further utilise the corpus co-occurrence statistics to propose two novel approaches, Nearest Neighbour Expansion (NNE) and Hedged Nearest Neighbour Expansion (HNE), that dynamically expand the KB and therefore derive more constraints that guide the optimisation process. Our experimental results over a wide-range of benchmark tasks demonstrate that the proposed method statistically significantly improves the accuracy of the word embeddings learnt. It outperforms a corpus-only baseline and reports an improvement of a number of previously proposed methods that incorporate corpora and KBs in both semantic similarity prediction and word analogy detection tasks. PMID:29529052

Modulation Based on Probability Density Functions

NASA Technical Reports Server (NTRS)

Williams, Glenn L.

2009-01-01

A proposed method of modulating a sinusoidal carrier signal to convey digital information involves the use of histograms representing probability density functions (PDFs) that characterize samples of the signal waveform. The method is based partly on the observation that when a waveform is sampled (whether by analog or digital means) over a time interval at least as long as one half cycle of the waveform, the samples can be sorted by frequency of occurrence, thereby constructing a histogram representing a PDF of the waveform during that time interval.

Deep learning and texture-based semantic label fusion for brain tumor segmentation

NASA Astrophysics Data System (ADS)

Vidyaratne, L.; Alam, M.; Shboul, Z.; Iftekharuddin, K. M.

2018-02-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

PubMed

Vidyaratne, L; Alam, M; Shboul, Z; Iftekharuddin, K M

2018-01-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully. Our proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data.

Spline based least squares integration for two-dimensional shape or wavefront reconstruction

DOE PAGES

Huang, Lei; Xue, Junpeng; Gao, Bo; ...

2016-12-21

In this paper, we present a novel method to handle two-dimensional shape or wavefront reconstruction from its slopes. The proposed integration method employs splines to fit the measured slope data with piecewise polynomials and uses the analytical polynomial functions to represent the height changes in a lateral spacing with the pre-determined spline coefficients. The linear least squares method is applied to estimate the height or wavefront as a final result. Numerical simulations verify that the proposed method has less algorithm errors than two other existing methods used for comparison. Especially at the boundaries, the proposed method has better performance. Themore » noise influence is studied by adding white Gaussian noise to the slope data. Finally, experimental data from phase measuring deflectometry are tested to demonstrate the feasibility of the new method in a practical measurement.« less

Simplified welding distortion analysis for fillet welding using composite shell elements

NASA Astrophysics Data System (ADS)

Kim, Mingyu; Kang, Minseok; Chung, Hyun

2015-09-01

This paper presents the simplified welding distortion analysis method to predict the welding deformation of both plate and stiffener in fillet welds. Currently, the methods based on equivalent thermal strain like Strain as Direct Boundary (SDB) has been widely used due to effective prediction of welding deformation. Regarding the fillet welding, however, those methods cannot represent deformation of both members at once since the temperature degree of freedom is shared at the intersection nodes in both members. In this paper, we propose new approach to simulate deformation of both members. The method can simulate fillet weld deformations by employing composite shell element and using different thermal expansion coefficients according to thickness direction with fixed temperature at intersection nodes. For verification purpose, we compare of result from experiments, 3D thermo elastic plastic analysis, SDB method and proposed method. Compared of experiments results, the proposed method can effectively predict welding deformation for fillet welds.

Spline based least squares integration for two-dimensional shape or wavefront reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Lei; Xue, Junpeng; Gao, Bo

In this paper, we present a novel method to handle two-dimensional shape or wavefront reconstruction from its slopes. The proposed integration method employs splines to fit the measured slope data with piecewise polynomials and uses the analytical polynomial functions to represent the height changes in a lateral spacing with the pre-determined spline coefficients. The linear least squares method is applied to estimate the height or wavefront as a final result. Numerical simulations verify that the proposed method has less algorithm errors than two other existing methods used for comparison. Especially at the boundaries, the proposed method has better performance. Themore » noise influence is studied by adding white Gaussian noise to the slope data. Finally, experimental data from phase measuring deflectometry are tested to demonstrate the feasibility of the new method in a practical measurement.« less

A new modulated Hebbian learning rule--biologically plausible method for local computation of a principal subspace.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2003-08-01

This paper presents one possible implementation of a transformation that performs linear mapping to a lower-dimensional subspace. Principal component subspace will be the one that will be analyzed. Idea implemented in this paper represents generalization of the recently proposed infinity OH neural method for principal component extraction. The calculations in the newly proposed method are performed locally--a feature which is usually considered as desirable from the biological point of view. Comparing to some other wellknown methods, proposed synaptic efficacy learning rule requires less information about the value of the other efficacies to make single efficacy modification. Synaptic efficacies are modified by implementation of Modulated Hebb-type (MH) learning rule. Slightly modified MH algorithm named Modulated Hebb Oja (MHO) algorithm, will be also introduced. Structural similarity of the proposed network with part of the retinal circuit will be presented, too.

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

NASA Astrophysics Data System (ADS)

Kim, Deok-Hwan

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

A multiple distributed representation method based on neural network for biomedical event extraction.

PubMed

Wang, Anran; Wang, Jian; Lin, Hongfei; Zhang, Jianhai; Yang, Zhihao; Xu, Kan

2017-12-20

Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.

Force Rendering and its Evaluation of a Friction-Based Walking Sensation Display for a Seated User.

PubMed

Kato, Ginga; Kuroda, Yoshihiro; Kiyokawa, Kiyoshi; Takemura, Haruo

2018-04-01

Most existing locomotion devices that represent the sensation of walking target a user who is actually performing a walking motion. Here, we attempted to represent the walking sensation, especially a kinesthetic sensation and advancing feeling (the sense of moving forward) while the user remains seated. To represent the walking sensation using a relatively simple device, we focused on the force rendering and its evaluation of the longitudinal friction force applied on the sole during walking. Based on the measurement of the friction force applied on the sole during actual walking, we developed a novel friction force display that can present the friction force without the influence of body weight. Using performance evaluation testing, we found that the proposed method can stably and rapidly display friction force. Also, we developed a virtual reality (VR) walk-through system that is able to present the friction force through the proposed device according to the avatar's walking motion in a virtual world. By evaluating the realism, we found that the proposed device can represent a more realistic advancing feeling than vibration feedback.

Dirac delta representation by exact parametric equations.. Application to impulsive vibration systems

NASA Astrophysics Data System (ADS)

Chicurel-Uziel, Enrique

2007-08-01

A pair of closed parametric equations are proposed to represent the Heaviside unit step function. Differentiating the step equations results in two additional parametric equations, that are also hereby proposed, to represent the Dirac delta function. These equations are expressed in algebraic terms and are handled by means of elementary algebra and elementary calculus. The proposed delta representation complies exactly with the values of the definition. It complies also with the sifting property and the requisite unit area and its Laplace transform coincides with the most general form given in the tables. Furthermore, it leads to a very simple method of solution of impulsive vibrating systems either linear or belonging to a large class of nonlinear problems. Two example solutions are presented.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

Infrared face recognition based on LBP histogram and KW feature selection

NASA Astrophysics Data System (ADS)

Xie, Zhihua

2014-07-01

The conventional LBP-based feature as represented by the local binary pattern (LBP) histogram still has room for performance improvements. This paper focuses on the dimension reduction of LBP micro-patterns and proposes an improved infrared face recognition method based on LBP histogram representation. To extract the local robust features in infrared face images, LBP is chosen to get the composition of micro-patterns of sub-blocks. Based on statistical test theory, Kruskal-Wallis (KW) feature selection method is proposed to get the LBP patterns which are suitable for infrared face recognition. The experimental results show combination of LBP and KW features selection improves the performance of infrared face recognition, the proposed method outperforms the traditional methods based on LBP histogram, discrete cosine transform(DCT) or principal component analysis(PCA).

Position Accuracy Analysis of a Robust Vision-Based Navigation

NASA Astrophysics Data System (ADS)

Gaglione, S.; Del Pizzo, S.; Troisi, S.; Angrisano, A.

2018-05-01

Using images to determine camera position and attitude is a consolidated method, very widespread for application like UAV navigation. In harsh environment, where GNSS could be degraded or denied, image-based positioning could represent a possible candidate for an integrated or alternative system. In this paper, such method is investigated using a system based on single camera and 3D maps. A robust estimation method is proposed in order to limit the effect of blunders or noisy measurements on position solution. The proposed approach is tested using images collected in an urban canyon, where GNSS positioning is very unaccurate. A previous photogrammetry survey has been performed to build the 3D model of tested area. The position accuracy analysis is performed and the effect of the robust method proposed is validated.

Explicitly represented polygon wall boundary model for the explicit MPS method

NASA Astrophysics Data System (ADS)

Mitsume, Naoto; Yoshimura, Shinobu; Murotani, Kohei; Yamada, Tomonori

2015-05-01

This study presents an accurate and robust boundary model, the explicitly represented polygon (ERP) wall boundary model, to treat arbitrarily shaped wall boundaries in the explicit moving particle simulation (E-MPS) method, which is a mesh-free particle method for strong form partial differential equations. The ERP model expresses wall boundaries as polygons, which are explicitly represented without using the distance function. These are derived so that for viscous fluids, and with less computational cost, they satisfy the Neumann boundary condition for the pressure and the slip/no-slip condition on the wall surface. The proposed model is verified and validated by comparing computed results with the theoretical solution, results obtained by other models, and experimental results. Two simulations with complex boundary movements are conducted to demonstrate the applicability of the E-MPS method to the ERP model.

A duality approach for solving bounded linear programming problems with fuzzy variables based on ranking functions and its application in bounded transportation problems

NASA Astrophysics Data System (ADS)

Ebrahimnejad, Ali

2015-08-01

There are several methods, in the literature, for solving fuzzy variable linear programming problems (fuzzy linear programming in which the right-hand-side vectors and decision variables are represented by trapezoidal fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings a new method based on the bounded dual simplex method is proposed to determine the fuzzy optimal solution of that kind of fuzzy variable linear programming problems in which some or all variables are restricted to lie within lower and upper bounds. To illustrate the proposed method, an application example is solved and the obtained results are given. The advantages of the proposed method over existing methods are discussed. Also, one application of this algorithm in solving bounded transportation problems with fuzzy supplies and demands is dealt with. The proposed method is easy to understand and to apply for determining the fuzzy optimal solution of bounded fuzzy variable linear programming problems occurring in real-life situations.

A new method for wind speed forecasting based on copula theory.

PubMed

Wang, Yuankun; Ma, Huiqun; Wang, Dong; Wang, Guizuo; Wu, Jichun; Bian, Jinyu; Liu, Jiufu

2018-01-01

How to determine representative wind speed is crucial in wind resource assessment. Accurate wind resource assessments are important to wind farms development. Linear regressions are usually used to obtain the representative wind speed. However, terrain flexibility of wind farm and long distance between wind speed sites often lead to low correlation. In this study, copula method is used to determine the representative year's wind speed in wind farm by interpreting the interaction of the local wind farm and the meteorological station. The result shows that the method proposed here can not only determine the relationship between the local anemometric tower and nearby meteorological station through Kendall's tau, but also determine the joint distribution without assuming the variables to be independent. Moreover, the representative wind data can be obtained by the conditional distribution much more reasonably. We hope this study could provide scientific reference for accurate wind resource assessments. Copyright © 2017 Elsevier Inc. All rights reserved.

A Simulation Analysis of an Extension of One-Dimensional Speckle Correlation Method for Detection of General In-Plane Translation

PubMed Central

Hrabovský, Miroslav

2014-01-01

The purpose of the study is to show a proposal of an extension of a one-dimensional speckle correlation method, which is primarily intended for determination of one-dimensional object's translation, for detection of general in-plane object's translation. In that view, a numerical simulation of a displacement of the speckle field as a consequence of general in-plane object's translation is presented. The translation components a x and a y representing the projections of a vector a of the object's displacement onto both x- and y-axes in the object plane (x, y) are evaluated separately by means of the extended one-dimensional speckle correlation method. Moreover, one can perform a distinct optimization of the method by reduction of intensity values representing detected speckle patterns. The theoretical relations between the translation components a x and a y of the object and the displacement of the speckle pattern for selected geometrical arrangement are mentioned and used for the testifying of the proposed method's rightness. PMID:24592180

Using formal methods to scope performance challenges for Smart Manufacturing Systems: focus on agility.

PubMed

Jung, Kiwook; Morris, K C; Lyons, Kevin W; Leong, Swee; Cho, Hyunbo

2015-12-01

Smart Manufacturing Systems (SMS) need to be agile to adapt to new situations by using detailed, precise, and appropriate data for intelligent decision-making. The intricacy of the relationship of strategic goals to operational performance across the many levels of a manufacturing system inhibits the realization of SMS. This paper proposes a method for identifying what aspects of a manufacturing system should be addressed to respond to changing strategic goals. The method uses standard modeling techniques in specifying a manufacturing system and the relationship between strategic goals and operational performance metrics. Two existing reference models related to manufacturing operations are represented formally and harmonized to support the proposed method. The method is illustrated for a single scenario using agility as a strategic goal. By replicating the proposed method for other strategic goals and with multiple scenarios, a comprehensive set of performance challenges can be identified.

Using formal methods to scope performance challenges for Smart Manufacturing Systems: focus on agility

PubMed Central

Jung, Kiwook; Morris, KC; Lyons, Kevin W.; Leong, Swee; Cho, Hyunbo

2016-01-01

Smart Manufacturing Systems (SMS) need to be agile to adapt to new situations by using detailed, precise, and appropriate data for intelligent decision-making. The intricacy of the relationship of strategic goals to operational performance across the many levels of a manufacturing system inhibits the realization of SMS. This paper proposes a method for identifying what aspects of a manufacturing system should be addressed to respond to changing strategic goals. The method uses standard modeling techniques in specifying a manufacturing system and the relationship between strategic goals and operational performance metrics. Two existing reference models related to manufacturing operations are represented formally and harmonized to support the proposed method. The method is illustrated for a single scenario using agility as a strategic goal. By replicating the proposed method for other strategic goals and with multiple scenarios, a comprehensive set of performance challenges can be identified. PMID:27141209

A modified precise integration method for transient dynamic analysis in structural systems with multiple damping models

NASA Astrophysics Data System (ADS)

Ding, Zhe; Li, Li; Hu, Yujin

2018-01-01

Sophisticated engineering systems are usually assembled by subcomponents with significantly different levels of energy dissipation. Therefore, these damping systems often contain multiple damping models and lead to great difficulties in analyzing. This paper aims at developing a time integration method for structural systems with multiple damping models. The dynamical system is first represented by a generally damped model. Based on this, a new extended state-space method for the damped system is derived. A modified precise integration method with Gauss-Legendre quadrature is then proposed. The numerical stability and accuracy of the proposed integration method are discussed in detail. It is verified that the method is conditionally stable and has inherent algorithmic damping, period error and amplitude decay. Numerical examples are provided to assess the performance of the proposed method compared with other methods. It is demonstrated that the method is more accurate than other methods with rather good efficiency and the stable condition is easy to be satisfied in practice.

Identification of differential pathways in papillary thyroid carcinoma utilizing pathway co-expression analysis.

PubMed

Qiu, Wei-Hai; Chen, Gui-Yan; Cui, Lu; Zhang, Ting-Ming; Wei, Feng; Yang, Yong

2016-01-01

To identify differential pathways between papillary thyroid carcinoma (PTC) patients and normal controls utilizing a novel method which combined pathway with co-expression network. The proposed method included three steps. In the first step, we conducted pretreatments for background pathways and gained representative pathways in PTC. Subsequently, a co-expression network for representative pathways was constructed using empirical Bayes (EB) approach to assign a weight value for each pathway. Finally, random model was extracted to set the thresholds of identifying differential pathways. We obtained 1267 representative pathways and their weight values based on the co-expressed pathway network, and then by meeting the criterion (Weight > 0.0296), 87 differential pathways in total across PTC patients and normal controls were identified. The top three ranked differential pathways were CREB phosphorylation, attachment of GPI anchor to urokinase plasminogen activator receptor (uPAR) and loss of function of SMAD2/3 in cancer. In conclusion, we successfully identified differential pathways (such as CREB phosphorylation, attachment of GPI anchor to uPAR and post-translational modification: synthesis of GPI-anchored proteins) for PTC using the proposed pathway co-expression method, and these pathways might be potential biomarkers for target therapy and detection of PTC.

Probabilistic topic modeling for the analysis and classification of genomic sequences

PubMed Central

2015-01-01

Background Studies on genomic sequences for classification and taxonomic identification have a leading role in the biomedical field and in the analysis of biodiversity. These studies are focusing on the so-called barcode genes, representing a well defined region of the whole genome. Recently, alignment-free techniques are gaining more importance because they are able to overcome the drawbacks of sequence alignment techniques. In this paper a new alignment-free method for DNA sequences clustering and classification is proposed. The method is based on k-mers representation and text mining techniques. Methods The presented method is based on Probabilistic Topic Modeling, a statistical technique originally proposed for text documents. Probabilistic topic models are able to find in a document corpus the topics (recurrent themes) characterizing classes of documents. This technique, applied on DNA sequences representing the documents, exploits the frequency of fixed-length k-mers and builds a generative model for a training group of sequences. This generative model, obtained through the Latent Dirichlet Allocation (LDA) algorithm, is then used to classify a large set of genomic sequences. Results and conclusions We performed classification of over 7000 16S DNA barcode sequences taken from Ribosomal Database Project (RDP) repository, training probabilistic topic models. The proposed method is compared to the RDP tool and Support Vector Machine (SVM) classification algorithm in a extensive set of trials using both complete sequences and short sequence snippets (from 400 bp to 25 bp). Our method reaches very similar results to RDP classifier and SVM for complete sequences. The most interesting results are obtained when short sequence snippets are considered. In these conditions the proposed method outperforms RDP and SVM with ultra short sequences and it exhibits a smooth decrease of performance, at every taxonomic level, when the sequence length is decreased. PMID:25916734

Combining 3d Volume and Mesh Models for Representing Complicated Heritage Buildings

NASA Astrophysics Data System (ADS)

Tsai, F.; Chang, H.; Lin, Y.-W.

2017-08-01

This study developed a simple but effective strategy to combine 3D volume and mesh models for representing complicated heritage buildings and structures. The idea is to seamlessly integrate 3D parametric or polyhedral models and mesh-based digital surfaces to generate a hybrid 3D model that can take advantages of both modeling methods. The proposed hybrid model generation framework is separated into three phases. Firstly, after acquiring or generating 3D point clouds of the target, these 3D points are partitioned into different groups. Secondly, a parametric or polyhedral model of each group is generated based on plane and surface fitting algorithms to represent the basic structure of that region. A "bare-bones" model of the target can subsequently be constructed by connecting all 3D volume element models. In the third phase, the constructed bare-bones model is used as a mask to remove points enclosed by the bare-bones model from the original point clouds. The remaining points are then connected to form 3D surface mesh patches. The boundary points of each surface patch are identified and these boundary points are projected onto the surfaces of the bare-bones model. Finally, new meshes are created to connect the projected points and original mesh boundaries to integrate the mesh surfaces with the 3D volume model. The proposed method was applied to an open-source point cloud data set and point clouds of a local historical structure. Preliminary results indicated that the reconstructed hybrid models using the proposed method can retain both fundamental 3D volume characteristics and accurate geometric appearance with fine details. The reconstructed hybrid models can also be used to represent targets in different levels of detail according to user and system requirements in different applications.

The use and generation of illustrative examples in computer-based instructional systems

NASA Technical Reports Server (NTRS)

Selig, William John; Johannes, James D.

1987-01-01

A method is proposed whereby the underlying domain knowledge is represented such that illustrative examples may be generated on demand. This method has the advantage that the generated example can follow changes in the domain in addition to allowing automatic customization of the example to the individual.

A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data.

PubMed

Silva Filho, Telmo M; Souza, Renata M C R; Prudêncio, Ricardo B C

2016-08-01

Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than classic point data for many domains. This paper proposes a new prototype-based classifier for interval data, trained by a swarm optimization method. Our work has two main contributions: a swarm method which is capable of performing both automatic selection of features and pruning of unused prototypes and a generalized weighted squared Euclidean distance for interval data. By discarding unnecessary features and prototypes, the proposed algorithm deals with typical limitations of prototype-based methods, such as the problem of prototype initialization. The proposed distance is useful for learning classes in interval datasets with different shapes, sizes and structures. When compared to other prototype-based methods, the proposed method achieves lower error rates in both synthetic and real interval datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

Contact-free heart rate measurement using multiple video data

NASA Astrophysics Data System (ADS)

Hung, Pang-Chan; Lee, Kual-Zheng; Tsai, Luo-Wei

2013-10-01

In this paper, we propose a contact-free heart rate measurement method by analyzing sequential images of multiple video data. In the proposed method, skin-like pixels are firstly detected from multiple video data for extracting the color features. These color features are synchronized and analyzed by independent component analysis. A representative component is finally selected among these independent component candidates to measure the HR, which achieves under 2% deviation on average compared with a pulse oximeter in the controllable environment. The advantages of the proposed method include: 1) it uses low cost and high accessibility camera device; 2) it eases users' discomfort by utilizing contact-free measurement; and 3) it achieves the low error rate and the high stability by integrating multiple video data.

On the Use of a Signal Quality Index Applying at Tracking Stage Level to Assist the RAIM System of a GNSS Receiver.

PubMed

Berardo, Mattia; Lo Presti, Letizia

2016-07-02

In this work, a novel signal processing method is proposed to assist the Receiver Autonomous Integrity Monitoring (RAIM) module used in a receiver of Global Navigation Satellite Systems (GNSS) to improve the integrity of the estimated position. The proposed technique represents an evolution of the Multipath Distance Detector (MPDD), thanks to the introduction of a Signal Quality Index (SQI), which is both a metric able to evaluate the goodness of the signal, and a parameter used to improve the performance of the RAIM modules. Simulation results show the effectiveness of the proposed method.

The graph neural network model.

PubMed

Scarselli, Franco; Gori, Marco; Tsoi, Ah Chung; Hagenbuchner, Markus; Monfardini, Gabriele

2009-01-01

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.

Efficient dynamic graph construction for inductive semi-supervised learning.

PubMed

Dornaika, F; Dahbi, R; Bosaghzadeh, A; Ruichek, Y

2017-10-01

Most of graph construction techniques assume a transductive setting in which the whole data collection is available at construction time. Addressing graph construction for inductive setting, in which data are coming sequentially, has received much less attention. For inductive settings, constructing the graph from scratch can be very time consuming. This paper introduces a generic framework that is able to make any graph construction method incremental. This framework yields an efficient and dynamic graph construction method that adds new samples (labeled or unlabeled) to a previously constructed graph. As a case study, we use the recently proposed Two Phase Weighted Regularized Least Square (TPWRLS) graph construction method. The paper has two main contributions. First, we use the TPWRLS coding scheme to represent new sample(s) with respect to an existing database. The representative coefficients are then used to update the graph affinity matrix. The proposed method not only appends the new samples to the graph but also updates the whole graph structure by discovering which nodes are affected by the introduction of new samples and by updating their edge weights. The second contribution of the article is the application of the proposed framework to the problem of graph-based label propagation using multiple observations for vision-based recognition tasks. Experiments on several image databases show that, without any significant loss in the accuracy of the final classification, the proposed dynamic graph construction is more efficient than the batch graph construction. Copyright © 2017 Elsevier Ltd. All rights reserved.

Establishment method of a mixture model and its practical application for transmission gears in an engineering vehicle

NASA Astrophysics Data System (ADS)

Wang, Jixin; Wang, Zhenyu; Yu, Xiangjun; Yao, Mingyao; Yao, Zongwei; Zhang, Erping

2012-09-01

Highly versatile machines, such as wheel loaders, forklifts, and mining haulers, are subject to many kinds of working conditions, as well as indefinite factors that lead to the complexity of the load. The load probability distribution function (PDF) of transmission gears has many distributions centers; thus, its PDF cannot be well represented by just a single-peak function. For the purpose of representing the distribution characteristics of the complicated phenomenon accurately, this paper proposes a novel method to establish a mixture model. Based on linear regression models and correlation coefficients, the proposed method can be used to automatically select the best-fitting function in the mixture model. Coefficient of determination, the mean square error, and the maximum deviation are chosen and then used as judging criteria to describe the fitting precision between the theoretical distribution and the corresponding histogram of the available load data. The applicability of this modeling method is illustrated by the field testing data of a wheel loader. Meanwhile, the load spectra based on the mixture model are compiled. The comparison results show that the mixture model is more suitable for the description of the load-distribution characteristics. The proposed research improves the flexibility and intelligence of modeling, reduces the statistical error and enhances the fitting accuracy, and the load spectra complied by this method can better reflect the actual load characteristic of the gear component.

Writing Abstracts for MLIS Research Proposals Using Worked Examples: An Innovative Approach to Teaching the Elements of Research Design

ERIC Educational Resources Information Center

Ondrusek, Anita L.; Thiele, Harold E.; Yang, Changwoo

2014-01-01

The authors examined abstracts written by graduate students for their research proposals as a requirement for a course in research methods in a distance learning MLIS program. The students learned under three instructional conditions that involved varying levels of access to worked examples created from abstracts representing research in the LIS…

A channel differential EZW coding scheme for EEG data compression.

PubMed

Dehkordi, Vahid R; Daou, Hoda; Labeau, Fabrice

2011-11-01

In this paper, a method is proposed to compress multichannel electroencephalographic (EEG) signals in a scalable fashion. Correlation between EEG channels is exploited through clustering using a k-means method. Representative channels for each of the clusters are encoded individually while other channels are encoded differentially, i.e., with respect to their respective cluster representatives. The compression is performed using the embedded zero-tree wavelet encoding adapted to 1-D signals. Simulations show that the scalable features of the scheme lead to a flexible quality/rate tradeoff, without requiring detailed EEG signal modeling.

The review and results of different methods for facial recognition

NASA Astrophysics Data System (ADS)

Le, Yifan

2017-09-01

In recent years, facial recognition draws much attention due to its wide potential applications. As a unique technology in Biometric Identification, facial recognition represents a significant improvement since it could be operated without cooperation of people under detection. Hence, facial recognition will be taken into defense system, medical detection, human behavior understanding, etc. Several theories and methods have been established to make progress in facial recognition: (1) A novel two-stage facial landmark localization method is proposed which has more accurate facial localization effect under specific database; (2) A statistical face frontalization method is proposed which outperforms state-of-the-art methods for face landmark localization; (3) It proposes a general facial landmark detection algorithm to handle images with severe occlusion and images with large head poses; (4) There are three methods proposed on Face Alignment including shape augmented regression method, pose-indexed based multi-view method and a learning based method via regressing local binary features. The aim of this paper is to analyze previous work of different aspects in facial recognition, focusing on concrete method and performance under various databases. In addition, some improvement measures and suggestions in potential applications will be put forward.

Linguistic multi-criteria decision-making with representing semantics by programming

NASA Astrophysics Data System (ADS)

Yang, Wu-E.; Ma, Chao-Qun; Han, Zhi-Qiu

2017-01-01

A linguistic multi-criteria decision-making method is introduced. In this method, a maximising discrimination programming assigns the semanteme values to linguistic variables to represent their semantics. Incomplete preferences from using linguistic information are expressed by the constraints of the model. Such assignment can amplify the difference between alternatives. Thus, the discrimination of the decision model is increased, which facilitates the decision-maker to rank or order the alternatives for making a decision. We also discuss the parameter setting and its influence, and use an application example to illustrate the proposed method. Further, the results with three types of semantic structure highlight the ability of the method in handling different semantic structures.

Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems

NASA Astrophysics Data System (ADS)

Peng, Juan-juan; Wang, Jian-qiang; Wang, Jing; Zhang, Hong-yu; Chen, Xiao-hong

2016-07-01

As a variation of fuzzy sets and intuitionistic fuzzy sets, neutrosophic sets have been developed to represent uncertain, imprecise, incomplete and inconsistent information that exists in the real world. Simplified neutrosophic sets (SNSs) have been proposed for the main purpose of addressing issues with a set of specific numbers. However, there are certain problems regarding the existing operations of SNSs, as well as their aggregation operators and the comparison methods. Therefore, this paper defines the novel operations of simplified neutrosophic numbers (SNNs) and develops a comparison method based on the related research of intuitionistic fuzzy numbers. On the basis of these operations and the comparison method, some SNN aggregation operators are proposed. Additionally, an approach for multi-criteria group decision-making (MCGDM) problems is explored by applying these aggregation operators. Finally, an example to illustrate the applicability of the proposed method is provided and a comparison with some other methods is made.

Joint sparse representation for robust multimodal biometrics recognition.

PubMed

Shekhar, Sumit; Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama

2014-01-01

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.

Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis-Taguchi system

NASA Astrophysics Data System (ADS)

Chen, Junxun; Cheng, Longsheng; Yu, Hui; Hu, Shaolin

2018-01-01